In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 × 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8.

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The dynamic responses of the high-speed railway bridge under the train passage can greatly affect the safety of the entire high-speed train and bridge system. Traditionally, these responses are obtained using either field measurement or numerical analysis. Both tools have their own limitations. For instance, the coupling dynamic train-bridge analysis is generally complicated and time-consuming. This paper proposes a novel machine learning based approach for efficient safety evaluation of the high speed train and short span bridge system. Artificial neural networks are established to map the complicated train-bridge system and to attain the critical bridge displacements. The proposed approach incorporates a complete numerical train-bridge system model to produce reliable data for the neural network training, considering multiple significant random features in the train-bridge system. Various neural network architectures are investigated and compared to find optimal ones that have considerable potentials in realizing online response prediction and safety evaluation. Although the proposed approach focuses on the high-speed train and short span bridge, the methodology is general and can also be applied to other scenarios associated with the vehicle-bridge systems.

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